Tag Archives: london

Benn Finn has been battling issues with his sleep ever since he was a teenager. His sleep was suffering from the usual problems we’ve all faced: taking too long to get to sleep, waking up too often, waking up late, and being tired during the day. He made plan to fix his issues by researching what affects sleep and then experimenting to find out what worked for him. For four months he tracked his sleep using Sleep Cycle along with 21 factors that he thought might affect his sleep. He also created a “sleep quality” score based on 5 different data points, including data from the Sleep Cycle app. In this talk, presented at the London QS meetup group, Ben describes his experiments, what he learned from analyzing his data, and how he finally ended up fixing his sleep issues. (Special thank you to Ken Snyder for his valuable work documenting the talks at QS London.)

In 2009 Tim Ngwena switched on Last.fm and he’s been running in across all his devices ever since. Earlier this year he decided to take a deep dive into his listening data to see what he could learn.

I realized that I was listening to the same old thing and I began to think about changing what I was listening to. But how can I change? Where can I start? I also wanted to learn something about my music, what I was listening to and who was behind the sounds. I decided to focus on music because it was doable.

In this talk, presented at the London QS meetup group, Tim explains how he was able to make sense of almost five years of data and learn more about himself and his listening habits.

What Did Tim Do?
Tim explored his music data along side additional information such as location data from Moves to learn about his musical tastes, listening habits, and explore new visualization and data analysis techniques.

How Did He Do It?
Tim exported his data, used the Last.fm API and some data cleaning and organizational tools to create a simplified and extensive database of his music listening history and associated data. He then visualized that data using Tableau.

What Did He Learn?
Tim learned a lot about himself and what the music he listens to says about him. He describes a few of the most interesting below,

Basically 80% of my listening comes form 10% of the artists that I have in my library.

I’ve listened to Erykah Badu for over a week (7.2 days). It led me to ask what is she saying to me?

Monday is my jam time. I’m listening from the morning into the evening.

I listen to music mostly when I’m walking.

Tim also learned a lot through the process of designing and creating his data visualization. The visualization, which you can explore here, made him think about being able to see the big picture when he has so much linked data.

I think context is important and you need to see all that information in one place and the tools I’m using allows me to do this.

What Did Adam Do?
In general, Adam is dedicated lifelogger who’s been tracking what he’s doing for over a year. Adam cycled 990 miles from Lands End to John O’Groats with his father and brother over 14 days and tracked it along the way. Because he wasn’t able to “lug around his Mac” to complete his regular lifelogging he decided to update his custom system to accept photos and notes. Lastly, he added habit tracking to his daily lifelogging experience by using the Lift app.

How Did He Do It?
Adam tracked his long distance cycling journey by using Google location history and a Garmin GPS unit. He was able to export data from both services in order to get a clear picture of his route as well as interesting data about the trip.

He also updated his lifelogging software so that it could accept photos and notes he hand enters on his phone. The software, available on GitHub, gives him an easy way to track multiple event such as how often he drinks alcohol and how much he has to use his asthma inhaler.

Lastly, Adam tracked the daily habits he wanted to accomplish such as meditating, reading, making three positive observations, and diet, using Lift.

What Did He Learn?
Everything Adam learned is based on his ability to access and export his data for further analysis. From his cycling trip he was able to make a simple map to showcase how far he traveled based on Google location history (which did have some issues with accuracy). He also was able to see that he traveled 1,004 miles, cycled for 90 hours, burned 52,000 calories, but didn’t lose any weight.

Using his updated lifelogging system, he was able to explore his inhaler use and after a visit to the doctor was able to “find out a boring correlation” that a preventative inhaler works and his exercise induced inhaler usage went to almost zero.

Finally, because Lift supports a robust data export, Adam was able to analyze his habit data and began answering questions he was interested in, but aren’t available in the native app experience. He found that seeing a visualization of his streaks as a cumulative graph was inspiring and motivating. He also explored his failures and found that Saturdays, Sundays, and Mondays were the days he was most likely to fail at completing at least one of his habits.

We’ve heard from our friend, and Pittsburgh QS meetup co-organizer, Anne Wright, manytimes before. She’s a wonderful proponent of the power of self-tracking and using data, research, and continuous exploration to discover and learn about what is meaningful in your life. All of that passion stems from a personal experience with overcoming various health issues. In this talk, presented at the London QS meetup group, Anne talks about how self-tracking played the key role in helping her recover. Anne then goes on to make the case for using self-tracking to learn how to forge your own unique path towards understanding in a world built around the idea of what is normal.

Jamie Aspinall was interested in what his location history could tell him. As a Google Location user, his smartphone is constantly pinging his GPS and sending that data back to his Google profile. Using Google Takeout Jamie was able to download the last four years of his location history, which represented about 600,000 data points. In this talk, presented at the London QS meetup group, Jamie describes his process of using a variety of visualizations and analysis techniques to learn about where he goes, what causes differences in his commute times, and other interesting patterns hidden in location data.

The excellent organizers of the London Quantified Self Show&Tell recently fielded a detailed survey about the self-tracking practices in their group. In the video below Ulrich Atz presents their findings.

Some of the interesting results from the survey:

105 respondents (22 identified as female, 76 as male).

Over 500 unique tools were being used.

47% of the respondents are currently measuring weight (17% have in the past).

Pen & paper is being used by 28% of respondents.

90% of respondents who answered a question about data sharing would share their data (or share it for medical research).

The presentation is available online here (PDF) and an aggregate view of the survey results is also available for you to explore here. We’re excited to see and learn more from this interesting data set in the future.

Adi Andrei wanted to combine artificial intelligence, psychology, art, and storytelling for the purpose of self-discovery of the subconscious mind. In the video below, Adi explains why he’s focused on this, how to go about entering the subconscious, and what he’s learned about hacking it. (Filmed by the London QS Show&Tell meetup group.)

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J. Paul Neeley has done experiments on optimizing happiness, self-control, and most recently, puns! His mom and brother are great punsters, so he decided to measure how many puns happened over Thanksgiving weekend with his family. In the video below, J. Paul explains this fun experiment, shares what he learned about the pattern of puns, and warns that punning can be contagious! (Filmed by the London QS Show&Tell meetup group.)

Neil Bachelor has been tracking his daily learning for the past two and a half years, with 3,200 discrete learning events. One of his motivations for this is to create a data-based CV that reflects his real work and learning habits. Neil uses Faviki to bookmark things he’s learned. In the video below, he describes his process, shows different visualizations of his learning, and explains the challenges he faces in managing so much data. (Filmed by the London QS Show&Tell meetup group.)

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Jules Goldberg is a snorer, and estimates that he has spent 1/8th of his life snoring. The noise was bothering his wife, so he built an app called SnoreLab to quantify his snoring (mild, loud, or epic?) and help him reduce it. In the video below, Jules shares how he identified where his snoring was coming from, remedies he tried, and which ones made it better and worse. (Filmed by the London QS Show&Tell meetup group.)

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